Selective outcome reporting across psychopharmacotherapy randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: Selective reporting impairs the valid interpretation of trials and leads to bias with regards to the clinical evidence. We aimed to examine factors associated with selective reporting in psychopharmacotherapy trials and thus enable solutions to prevent such selective reporting in the future. METHODS: We retrieved all registry records of trials investigating medication for depressive, bipolar and psychotic disorders. Multivariate logistic regression was performed with selective reporting as outcome, and funding source, psychiatric disorder, year of study start date, participating centers, and anticipated sample size as explanatory variables, after testing for multicollinearity. Adjusted odds ratios (AOR) were calculated. Two-sided Fisher exact test was used to compare the proportions of newly added positive primary outcomes with the proportions of positive results in the overall group of primary outcomes. RESULTS: Of 151 included trials (N = 94,303 participants), 21 (14%) showed irregularities between registered and published primary outcomes. Higher odds of such irregularities were associated with non-industry-funded RCTs (AOR 5.3; p = 0.014) and trials investigating major depressive disorder (AOR 12.7; p = 0.024) or schizophrenia (AOR 14.5; p = 0.016; Table 1). CONCLUSION: We demonstrate discrepancies between trial registrations and publications across RCTs investigating debilitating psychiatric disorders, especially in non-industry funded RCTs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad)Research integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchResearch integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.932 | 0.872 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".